Point2Skeleton: Learning Skeletal Representations from Point Clouds

Cheng Lin, Changjian Li, Yuan Liu, Nenglun Chen, Yi-King Choi, Wenping Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We introduce Point2Skeleton, an unsupervised method to learn skeletal representations from point clouds. Existing skeletonization methods are limited to tubular shapes and the stringent requirement of watertight input, while our method aims to produce more generalized skeletal representations for complex structures and handle point clouds. Our key idea is to use the insights of the medial axis transform (MAT) to capture the intrinsic geometric and topological natures of the original input points. We first predict a set of skeletal points by learning a geometric transformation, and then analyze the connectivity of the skeletal points to form skeletal mesh structures. Extensive evaluations and comparisons show our method has superior performance and robustness. The learned skeletal representation will benefit several unsupervised tasks for point clouds, such as surface reconstruction and segmentation.
Original languageEnglish
Title of host publicationProceedings of the 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
PublisherInstitute of Electrical and Electronics Engineers
Pages4275-4284
Number of pages10
ISBN (Electronic)978-1-6654-4509-2
ISBN (Print)978-1-6654-4510-8
DOIs
Publication statusPublished - 2 Nov 2021
EventIEEE Conference on Computer Vision and Pattern Recognition 2021 - Virtual
Duration: 19 Jun 202125 Jun 2021
http://cvpr2021.thecvf.com/

Publication series

NameConference on Computer Vision and Pattern Recognition (CVPR)
PublisherIEEE
ISSN (Print)2575-7075
ISSN (Electronic)1063-6919

Conference

ConferenceIEEE Conference on Computer Vision and Pattern Recognition 2021
Abbreviated titleCVPR 2021
Period19/06/2125/06/21
Internet address

Fingerprint

Dive into the research topics of 'Point2Skeleton: Learning Skeletal Representations from Point Clouds'. Together they form a unique fingerprint.

Cite this